Testosteron/cort: Effects of ELS threat severity
summary(mod2_test_threat)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(cort_clean) ~ scale(test_per_cent) * scale(sumsev_threat_t1) +
scale(test_av_t1t2) * scale(sumsev_threat_t1) + (1 | ELS_ID)
Data: cd_clean
REML criterion at convergence: 681.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.78906 -0.57460 0.02752 0.59678 2.28282
Random effects:
Groups Name Variance Std.Dev.
ELS_ID (Intercept) 0.2441 0.4941
Residual 0.5763 0.7592
Number of obs: 256, groups: ELS_ID, 137
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.004361 0.063900 131.288257 0.068 0.94569
scale(test_per_cent) 0.307242 0.048004 124.817480 6.400 0.00000000284 ***
scale(sumsev_threat_t1) -0.023839 0.065974 129.820787 -0.361 0.71843
scale(test_av_t1t2) 0.284041 0.071270 134.453435 3.985 0.00011 ***
scale(test_per_cent):scale(sumsev_threat_t1) -0.116697 0.048175 124.470920 -2.422 0.01686 *
scale(sumsev_threat_t1):scale(test_av_t1t2) -0.012293 0.063104 131.428195 -0.195 0.84585
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(__) sc(__1) s(__12 s(__):
scl(tst_p_) -0.026
scl(sms__1) 0.036 -0.015
scl(ts__12) -0.003 -0.011 -0.045
s(__):(__1) -0.012 0.058 -0.029 -0.013
s(__1):(__1 -0.013 -0.017 0.145 -0.153 -0.033
confint.merMod(mod2_test_threat, method = "boot")
Computing bootstrap confidence intervals ...
1 warning(s): Model failed to converge with max|grad| = 0.00253107 (tol = 0.002, component 1)
2.5 % 97.5 %
.sig01 0.3154895 0.66061863
.sigma 0.6670611 0.85778966
(Intercept) -0.1261734 0.14042704
scale(test_per_cent) 0.2164492 0.40035091
scale(sumsev_threat_t1) -0.1559008 0.09747200
scale(test_av_t1t2) 0.1595182 0.43561248
scale(test_per_cent):scale(sumsev_threat_t1) -0.2123697 -0.02130992
scale(sumsev_threat_t1):scale(test_av_t1t2) -0.1321208 0.10965877
mod2_test_threat_t2 <-
lmer(
scale(cort_clean) ~
scale(test_per_cent) * scale(sumsev_threat_t2) +
scale(test_av_t1t2) * scale(sumsev_threat_t2) +
(1 | ELS_ID),
data = cd_clean
)
summary(mod2_test_threat_t2)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(cort_clean) ~ scale(test_per_cent) * scale(sumsev_threat_t2) +
scale(test_av_t1t2) * scale(sumsev_threat_t2) + (1 | ELS_ID)
Data: cd_clean
REML criterion at convergence: 632.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.55994 -0.53660 0.01771 0.58563 2.46461
Random effects:
Groups Name Variance Std.Dev.
ELS_ID (Intercept) 0.2325 0.4822
Residual 0.6338 0.7961
Number of obs: 232, groups: ELS_ID, 124
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.02027 0.06828 118.54793 -0.297 0.767042
scale(test_per_cent) 0.34323 0.05462 112.55750 6.284 0.00000000644 ***
scale(sumsev_threat_t2) -0.04804 0.06976 121.55874 -0.689 0.492341
scale(test_av_t1t2) 0.27122 0.07576 118.73172 3.580 0.000499 ***
scale(test_per_cent):scale(sumsev_threat_t2) -0.03177 0.05442 110.63505 -0.584 0.560510
scale(sumsev_threat_t2):scale(test_av_t1t2) 0.03813 0.09247 117.00845 0.412 0.680836
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(__) sc(__2) s(__12 s(__):
scl(tst_p_) -0.019
scl(sms__2) 0.022 -0.002
scl(ts__12) 0.028 0.004 0.026
s(__):(__2) -0.002 0.018 -0.014 0.012
s(__2):(__1 0.032 0.015 0.075 -0.005 0.011
mod2_test_threat_boys <-
lmer(
scale(cort_clean) ~
scale(test_per_cent) * scale(sumsev_threat_t1) +
scale(test_av_t1t2) * scale(sumsev_threat_t1) +
(1 | ELS_ID),
data = cd_clean_boys
)
summary(mod2_test_threat_boys)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(cort_clean) ~ scale(test_per_cent) * scale(sumsev_threat_t1) +
scale(test_av_t1t2) * scale(sumsev_threat_t1) + (1 | ELS_ID)
Data: cd_clean_boys
REML criterion at convergence: 305.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.40026 -0.48992 0.08387 0.64318 1.62534
Random effects:
Groups Name Variance Std.Dev.
ELS_ID (Intercept) 0.1622 0.4027
Residual 0.4865 0.6975
Number of obs: 123, groups: ELS_ID, 64
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.04994 0.08277 59.67464 -0.603 0.5485
scale(test_per_cent) 0.41020 0.06416 60.00727 6.394 0.0000000268 ***
scale(sumsev_threat_t1) -0.14377 0.08448 59.60959 -1.702 0.0940 .
scale(test_av_t1t2) 0.45701 0.09573 65.30837 4.774 0.0000105802 ***
scale(test_per_cent):scale(sumsev_threat_t1) -0.08779 0.06161 60.70625 -1.425 0.1593
scale(sumsev_threat_t1):scale(test_av_t1t2) 0.26256 0.11252 68.71931 2.333 0.0226 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(__) sc(__1) s(__12 s(__):
scl(tst_p_) -0.019
scl(sms__1) 0.068 -0.023
scl(ts__12) -0.032 -0.025 -0.199
s(__):(__1) -0.018 0.088 -0.024 -0.033
s(__1):(__1 -0.209 -0.033 -0.128 0.213 -0.051
#confint.merMod(mod2_test_threat_boys, method = "boot")
# higher threat severity in boys________________________________________________
mod2_test_threat_boys_hi <-
lmer(
cort_clean ~
scale(test_per_cent, scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) +
scale(test_av_t1t2, scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) +
(1 | ELS_ID),
data = cd_clean_boys
)
summary(mod2_test_threat_boys_hi)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: cort_clean ~ scale(test_per_cent, scale = FALSE) * I(scale(sumsev_threat_t1,
scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) + scale(test_av_t1t2,
scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) -
sd(sumsev_threat_t1, na.rm = TRUE)) + (1 | ELS_ID)
Data: cd_clean_boys
REML criterion at convergence: 198.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.40026 -0.48992 0.08387 0.64318 1.62534
Random effects:
Groups Name Variance Std.Dev.
ELS_ID (Intercept) 0.06621 0.2573
Residual 0.19860 0.4456
Number of obs: 123, groups: ELS_ID, 64
Fixed effects:
Estimate
(Intercept) -1.69066
scale(test_per_cent, scale = FALSE) 0.44216
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) -0.04776
scale(test_av_t1t2, scale = FALSE) 0.85459
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) -0.06260
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.16212
Std. Error
(Intercept) 0.07810
scale(test_per_cent, scale = FALSE) 0.12720
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.02806
scale(test_av_t1t2, scale = FALSE) 0.19303
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.04393
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.06948
df
(Intercept) 60.79473
scale(test_per_cent, scale = FALSE) 62.05761
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 59.60959
scale(test_av_t1t2, scale = FALSE) 67.98183
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 60.70625
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 68.71931
t value
(Intercept) -21.648
scale(test_per_cent, scale = FALSE) 3.476
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) -1.702
scale(test_av_t1t2, scale = FALSE) 4.427
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) -1.425
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 2.333
Pr(>|t|)
(Intercept) < 0.0000000000000002
scale(test_per_cent, scale = FALSE) 0.000935
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.093982
scale(test_av_t1t2, scale = FALSE) 0.0000355
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.159270
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.022563
(Intercept) ***
scale(test_per_cent, scale = FALSE) ***
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) .
scale(test_av_t1t2, scale = FALSE) ***
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE))
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(__,s=F Is=F-sn=T s(__1s=F ss=Fs=F-sn=T
s(__,s=FALS -0.039
I((s=F-sn=T 0.737 -0.032
s(__12,s=FA -0.254 -0.062 -0.206
ss=Fs=F-sn=T -0.029 0.725 -0.024 -0.055
Is=F-sn=Ts=F -0.231 -0.057 -0.128 0.818 -0.051
mod2_test_threat_boys_hi_int <- summary(mod2_test_threat_boys_hi)$coefficients[1]
mod2_test_threat_boys_hi_slp <- summary(mod2_test_threat_boys_hi)$coefficients[2]
# lower threat severity in boys________________________________________________
mod2_test_threat_boys_lo <-
lmer(
cort_clean ~
scale(test_per_cent, scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) +
scale(test_av_t1t2, scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) +
(1 | ELS_ID),
data = cd_clean_boys
)
summary(mod2_test_threat_boys_lo)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: cort_clean ~ scale(test_per_cent, scale = FALSE) * I(scale(sumsev_threat_t1,
scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) + scale(test_av_t1t2,
scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) +
sd(sumsev_threat_t1, na.rm = TRUE)) + (1 | ELS_ID)
Data: cd_clean_boys
REML criterion at convergence: 198.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.40026 -0.48992 0.08387 0.64318 1.62534
Random effects:
Groups Name Variance Std.Dev.
ELS_ID (Intercept) 0.06621 0.2573
Residual 0.19860 0.4456
Number of obs: 123, groups: ELS_ID, 64
Fixed effects:
Estimate
(Intercept) -1.50694
scale(test_per_cent, scale = FALSE) 0.68296
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) -0.04776
scale(test_av_t1t2, scale = FALSE) 0.23094
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) -0.06260
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.16212
Std. Error
(Intercept) 0.07294
scale(test_per_cent, scale = FALSE) 0.11653
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.02806
scale(test_av_t1t2, scale = FALSE) 0.15592
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.04393
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.06948
df
(Intercept) 58.34553
scale(test_per_cent, scale = FALSE) 58.35685
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 59.60959
scale(test_av_t1t2, scale = FALSE) 66.19008
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 60.70625
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 68.71931
t value
(Intercept) -20.661
scale(test_per_cent, scale = FALSE) 5.861
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) -1.702
scale(test_av_t1t2, scale = FALSE) 1.481
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) -1.425
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 2.333
Pr(>|t|)
(Intercept) < 0.0000000000000002
scale(test_per_cent, scale = FALSE) 0.000000227
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.0940
scale(test_av_t1t2, scale = FALSE) 0.1433
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.1593
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.0226
(Intercept) ***
scale(test_per_cent, scale = FALSE) ***
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) .
scale(test_av_t1t2, scale = FALSE)
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE))
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(__,s=F Is=F+sn=T s(__1s=F ss=Fs=F+sn=T
s(__,s=FALS -0.001
I((s=F+sn=T -0.691 0.000
s(__12,s=FA 0.139 -0.006 -0.035
ss=Fs=F+sn=T 0.004 -0.659 -0.024 0.020
Is=F+sn=Ts=F -0.057 0.012 -0.128 -0.702 -0.051
mod2_test_threat_boys_lo_int <- summary(mod2_test_threat_boys_lo)$coefficients[1]
mod2_test_threat_boys_lo_slp <- summary(mod2_test_threat_boys_lo)$coefficients[2]
mod2_test_threat_girls <-
lmer(
scale(cort_clean) ~
scale(test_per_cent) * scale(sumsev_threat_t1) +
scale(test_av_t1t2) * scale(sumsev_threat_t1) +
(1 | ELS_ID),
data = cd_clean_girls
)
summary(mod2_test_threat_girls)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(cort_clean) ~ scale(test_per_cent) * scale(sumsev_threat_t1) +
scale(test_av_t1t2) * scale(sumsev_threat_t1) + (1 | ELS_ID)
Data: cd_clean_girls
REML criterion at convergence: 367.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.51504 -0.64320 -0.00077 0.52372 1.88116
Random effects:
Groups Name Variance Std.Dev.
ELS_ID (Intercept) 0.1859 0.4311
Residual 0.6722 0.8199
Number of obs: 133, groups: ELS_ID, 73
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.008466 0.088423 66.515888 0.096 0.924014
scale(test_per_cent) 0.180267 0.072494 66.239074 2.487 0.015423 *
scale(sumsev_threat_t1) 0.034255 0.093726 65.609394 0.365 0.715929
scale(test_av_t1t2) 0.369763 0.102718 68.159150 3.600 0.000599 ***
scale(test_per_cent):scale(sumsev_threat_t1) -0.175229 0.075262 62.992713 -2.328 0.023123 *
scale(sumsev_threat_t1):scale(test_av_t1t2) -0.066352 0.076732 64.356233 -0.865 0.390400
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) sc(__) sc(__1) s(__12 s(__):
scl(tst_p_) -0.030
scl(sms__1) 0.059 0.021
scl(ts__12) -0.049 -0.003 0.046
s(__):(__1) 0.012 -0.066 -0.021 0.036
s(__1):(__1 0.120 0.024 0.252 -0.320 -0.030
#confint.merMod(mod2_test_threat_girls, method = "boot")
# higher threat severity in girls________________________________________________
mod2_test_threat_girls_hi <-
lmer(
cort_clean ~
scale(test_per_cent, scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) +
scale(test_av_t1t2, scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) +
(1 | ELS_ID),
data = cd_clean_girls
)
summary(mod2_test_threat_girls_hi)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: cort_clean ~ scale(test_per_cent, scale = FALSE) * I(scale(sumsev_threat_t1,
scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) + scale(test_av_t1t2,
scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) -
sd(sumsev_threat_t1, na.rm = TRUE)) + (1 | ELS_ID)
Data: cd_clean_girls
REML criterion at convergence: 241.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.51504 -0.64320 -0.00077 0.52372 1.88116
Random effects:
Groups Name Variance Std.Dev.
ELS_ID (Intercept) 0.07199 0.2683
Residual 0.26039 0.5103
Number of obs: 133, groups: ELS_ID, 73
Fixed effects:
Estimate
(Intercept) -1.45668
scale(test_per_cent, scale = FALSE) 0.01254
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.01012
scale(test_av_t1t2, scale = FALSE) 0.52159
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) -0.20716
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) -0.05416
Std. Error
(Intercept) 0.08252
scale(test_per_cent, scale = FALSE) 0.25140
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.02770
scale(test_av_t1t2, scale = FALSE) 0.18347
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.08898
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.06264
df
(Intercept) 65.68829
scale(test_per_cent, scale = FALSE) 61.25590
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 65.60939
scale(test_av_t1t2, scale = FALSE) 63.76436
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 62.99271
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 64.35623
t value
(Intercept) -17.653
scale(test_per_cent, scale = FALSE) 0.050
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.365
scale(test_av_t1t2, scale = FALSE) 2.843
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) -2.328
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) -0.865
Pr(>|t|)
(Intercept) <0.0000000000000002
scale(test_per_cent, scale = FALSE) 0.9604
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.7159
scale(test_av_t1t2, scale = FALSE) 0.0060
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) 0.0231
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.3904
(Intercept) ***
scale(test_per_cent, scale = FALSE)
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE))
scale(test_av_t1t2, scale = FALSE) **
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)) *
I(scale(sumsev_threat_t1, scale = FALSE) - sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE)
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(__,s=F Is=F-sn=T s(__1s=F ss=Fs=F-sn=T
s(__,s=FALS -0.009
I((s=F-sn=T 0.746 -0.001
s(__12,s=FA 0.186 0.020 0.226
ss=Fs=F-sn=T -0.007 0.698 -0.021 0.013
Is=F-sn=Ts=F 0.258 -0.005 0.252 0.411 -0.030
mod2_test_threat_girls_hi_int <- summary(mod2_test_threat_girls_hi)$coefficients[1]
mod2_test_threat_girls_hi_slp <- summary(mod2_test_threat_girls_hi)$coefficients[2]
# lower threat severity in girls________________________________________________
mod2_test_threat_girls_lo <-
lmer(
cort_clean ~
scale(test_per_cent, scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) +
scale(test_av_t1t2, scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) +
(1 | ELS_ID),
data = cd_clean_girls
)
summary(mod2_test_threat_girls_lo)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: cort_clean ~ scale(test_per_cent, scale = FALSE) * I(scale(sumsev_threat_t1,
scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) + scale(test_av_t1t2,
scale = FALSE) * I(scale(sumsev_threat_t1, scale = FALSE) +
sd(sumsev_threat_t1, na.rm = TRUE)) + (1 | ELS_ID)
Data: cd_clean_girls
REML criterion at convergence: 241.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.51504 -0.64320 -0.00077 0.52372 1.88116
Random effects:
Groups Name Variance Std.Dev.
ELS_ID (Intercept) 0.07199 0.2683
Residual 0.26039 0.5103
Number of obs: 133, groups: ELS_ID, 73
Fixed effects:
Estimate
(Intercept) -1.49931
scale(test_per_cent, scale = FALSE) 0.88506
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.01012
scale(test_av_t1t2, scale = FALSE) 0.74972
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) -0.20716
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) -0.05416
Std. Error
(Intercept) 0.07780
scale(test_per_cent, scale = FALSE) 0.26864
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.02770
scale(test_av_t1t2, scale = FALSE) 0.25199
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.08898
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.06264
df
(Intercept) 66.42664
scale(test_per_cent, scale = FALSE) 67.54860
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 65.60939
scale(test_av_t1t2, scale = FALSE) 68.42245
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 62.99271
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 64.35623
t value
(Intercept) -19.271
scale(test_per_cent, scale = FALSE) 3.295
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.365
scale(test_av_t1t2, scale = FALSE) 2.975
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) -2.328
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) -0.865
Pr(>|t|)
(Intercept) < 0.0000000000000002
scale(test_per_cent, scale = FALSE) 0.00157
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.71593
scale(test_av_t1t2, scale = FALSE) 0.00404
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) 0.02312
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE) 0.39040
(Intercept) ***
scale(test_per_cent, scale = FALSE) **
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE))
scale(test_av_t1t2, scale = FALSE) **
scale(test_per_cent, scale = FALSE):I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)) *
I(scale(sumsev_threat_t1, scale = FALSE) + sd(sumsev_threat_t1, na.rm = TRUE)):scale(test_av_t1t2, scale = FALSE)
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) s(__,s=F Is=F+sn=T s(__1s=F ss=Fs=F+sn=T
s(__,s=FALS -0.042
I((s=F+sn=T -0.708 0.029
s(__12,s=FA 0.006 -0.038 -0.100
ss=Fs=F+sn=T 0.025 -0.742 -0.021 0.041
Is=F+sn=Ts=F -0.104 0.037 0.252 -0.748 -0.030
mod2_test_threat_girls_lo_int <- summary(mod2_test_threat_girls_lo)$coefficients[1]
mod2_test_threat_girls_lo_slp <- summary(mod2_test_threat_girls_lo)$coefficients[2]
plot_mod2_test <-
tibble(
Sex = c("Girls", "Boys", "Girls", "Boys"),
Threat = c("Higher (+1SD)", "Higher (+1SD)", "Lower (-1SD)", "Lower (-1SD)"),
Intercept = c(
mod2_test_threat_girls_hi_int,
mod2_test_threat_boys_hi_int,
mod2_test_threat_girls_lo_int,
mod2_test_threat_boys_lo_int
),
Slope = c(
mod2_test_threat_girls_hi_slp,
mod2_test_threat_boys_hi_slp,
mod2_test_threat_girls_lo_slp,
mod2_test_threat_boys_lo_slp
)
)
# produce a plot for legend only_______________________________________________
plot_mod2_test_threat_legend <-
cd_clean_boys %>%
ggplot(aes(test_per_cent, cort_clean)) +
geom_point(alpha = 1/2) +
geom_abline(
data = plot_mod2_test %>% filter(Sex == "Boys"),
aes(
intercept = Intercept,
slope = Slope,
color = Threat
),
size = 2
) +
scale_x_continuous(breaks = seq.int(-4, 4, 1)) +
scale_color_manual(
values = c("darkred", "darkblue")
) +
expand_limits(y = c(-1, -4)) +
expand_limits(x = c(-1, 1)) +
theme_apa(base_size = 20, box = TRUE) +
theme(
plot.title = element_text(hjust = .5)
) +
labs(
title = "Boys",
shape = NULL,
size = NULL,
color = "Threat severity",
x = NULL,
y = "log Cortisol (µg/dL)"
)
# plot for boys________________________________________________________________
plot_mod2_test_threat_boys <-
cd_clean_boys %>%
ggplot(aes(test_per_cent, cort_clean)) +
geom_point(alpha = 1/2, aes(size = sumsev_threat_t1, show.legend = FALSE)) +
geom_abline(
data = plot_mod2_test %>% filter(Sex == "Boys"),
aes(
intercept = Intercept,
slope = Slope,
color = Threat
),
size = 2
) +
scale_x_continuous(breaks = seq.int(-4, 4, 1)) +
scale_color_manual(
values = c("darkred", "darkblue")
) +
expand_limits(y = c(-1, -4)) +
expand_limits(x = c(-1, 1)) +
theme_apa(base_size = 20, box = TRUE) +
theme(
plot.title = element_text(hjust = .5)
) +
labs(
title = "Boys",
shape = NULL,
size = NULL,
color = "Threat severity",
x = NULL,
y = NULL
)
Ignoring unknown aesthetics: show.legend
ggsave(
"~/Box/lucy_king_files/ELS/cort_dhea/test_cort_threat_boys.png",
height = 6, width = 10
)
# plot for girls________________________________________________________________
plot_mod2_test_threat_girls <-
cd_clean_girls %>%
ggplot(aes(test_per_cent, cort_clean)) +
geom_point(alpha = 1/2, aes(size = sumsev_threat_t1), show.legend = FALSE) +
geom_abline(
data = plot_mod2_test %>% filter(Sex == "Girls"),
aes(
intercept = Intercept,
slope = Slope,
color = Threat
),
size = 2
) +
scale_x_continuous(breaks = seq.int(-4, 4, 1)) +
scale_color_manual(
values = c("darkred", "darkblue")
) +
expand_limits(y = c(-1, -4)) +
expand_limits(x = c(-1, 1)) +
theme_apa(base_size = 24, box = TRUE) +
theme(
plot.title = element_text(hjust = .5)
) +
labs(
title = "Girls",
shape = NULL,
size = NULL,
color = "Threat severity",
x = NULL,
y = NULL
)
ggsave(
"~/Box/lucy_king_files/ELS/cort_dhea/test_cort_threat_girls.png",
height = 6, width = 10
)
# save together________________________________________________________________
plot_mod2_test_threat_sex <-
plot_grid(
plot_mod2_test_threat_girls + theme(legend.position = "none"),
plot_mod2_test_threat_boys + theme(legend.position = "none"),
align = "hv",
axis = "bt",
rel_widths = c(1, 1)
)
#create common x and y labels___________________________________________________
y.grob_cort <- textGrob(
"log Cortisol (µg/dL)",
gp = gpar(col = "black", fontsize = 22),
rot = 90
)
x.grob_test <- textGrob(
expression("Panel B: "*Delta*" log testosterone (pg/mL) (person-mean-centered)"),
gp = gpar(col = "black", fontsize = 22)
)
#add common labels to plot______________________________________________________
plot_mod2_test_threat_sex <-
grid.arrange(
arrangeGrob(
plot_mod2_test_threat_sex,
left = y.grob_cort,
bottom = x.grob_test
)
)

# extract legend________________________________________________________________
threat_legend <- get_legend(
# create some space to the left of the legend
plot_mod2_test_threat_legend +
theme(
legend.box.margin = margin(0, 0, 0, 12)
)
)
Removed 25 rows containing missing values (geom_point).
# add legend back_______________________________________________________________
# the width of one plot (via rel_widths).
plot_grid(plot_mod2_test_threat_sex, threat_legend, rel_widths = c(1, .2))
# save plot_____________________________________________________________________
ggsave("~/Box/lucy_king_files/ELS/cort_dhea/test_cort_threat_sex.png", width = 12, height = 6)

# save DHEA and testosterone plots together
plot_mod2 <-
grid.arrange(plot_mod2_threat_sex, plot_mod2_test_threat_sex)

plot_grid(
plot_mod2,
threat_legend,
rows = 2,
rel_heights = c(5, 1)
)
Argument 'rows' is deprecated. Use 'nrow' instead.
ggsave("~/Box/lucy_king_files/ELS/cort_dhea/hormones_cort_threat_sex.png", width = 12, height = 12)
